Soil Environmental Factors Drive Seed Density Across Vegetation Types on the Tibetan Plateau

Plant Soil DOI 10.1007/s11104-017-3348-0

REGULAR ARTICLE

Soil environmental factors drive seed density across vegetation types on the Tibetan Plateau

Miaojun Ma & James W. Dalling & Zhen Ma & Xianhui Zhou

Received: 7 March 2017 /Accepted: 13 July 2017 # Springer International Publishing AG 2017

Abstract Methods Here we collected 1026 soil seed bank sam- Background and aims Soil seed banks are an impor- ples and sampled 171 aboveground vegetation quadrats tant source of seedling recruits in grassland ecosys- in 57 sites representing six distinct grass-dominated tems, particularly following large-scale disturbance. vegetation types present at high elevation on the Tibetan Nonetheless, the relative importance of above- Plateau, China. To understand processes affecting seed ground plant communities versus below-ground pro- banks at these sites we examined the associations of soil cesses in maintaining these seed banks is poorly environmental variables with community composition, understood. density and species richness. Results We found significant differences in species composition between the seed bank and standing Responsible Editor: Jeffrey Walck. vegetation in each of the vegetation types, whereas Electronic supplementary material The online version of this soil seed banks were much more similar to each article (doi:10.1007/s11104-017-3348-0) contains supplementary other. Nonetheless, seed bank composition was sig- material, which is available to authorized users. nificantly associated with soil moisture, pH, and M. Ma (*) : Z. Ma : X. Zhou available nitrogen (all p < 0.05). Seed density was State Key Laboratory of Grassland and Agro-ecosystems, School significantly negatively correlated with soil pH and of Life Sciences, Lanzhou University, Lanzhou, Gansu 730000, positively correlated with soil moisture and soil or- China e-mail: [email protected] ganic matter. Multiple regression analysis showed that for seed bank density a model including pH Z. Ma alone had the lowest AIC value. e-mail: [email protected] Conclusions Soil conditions influence not only seed inputs but also potentially seed survival in the soil, X. Zhou e-mail: [email protected] this study present a framework which supply a plau- sible explanation on effect of soil environment factors (chemical and physical factors) in the formation J. W. Dalling of species composition of soil seed bank. Future Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA work should isolate the direct and indirect effects e-mail: [email protected] of soil chemistry on seed persistence using seed burial experiments. Z. Ma Key Laboratory of Adaptation and Evolution of Plateau Biota, Northwest Institute of Plateau Biology, Chinese Academy of Keywords pH . Plant community. Similarity. Soil Sciences, Xining, Qinghai 810008, China environmental factor. Soil moisture . Soil seed bank Plant Soil

Introduction Lauber et al. 2008). In particular, the composition of fungal communities can be sensitive to variability in Soil seed banks represent a critical source of recruits soil nutrient availability (Lauber et al. 2008). In for many plant populations that occupy temporally contrast, the composition of soil bacterial communi- variable environments (Venable and Brown 1988). ties is more strongly related to soil pH (Fierer and For some species, seed persistence has been shown Jackson 2006; Hartman et al. 2008; Lauber et al. to be influenced by biotic (e.g. Schafer and Kotanen 2009), potentially reflecting a narrower optimal pH 2003, 2004; Dalling et al. 2011;Mordecai2012)or range for bacterial than fungal growth (Rousk et al. abiotic conditions in the soil (e.g. Pakeman et al. 2010). As yet, however, the effects of soil pH on 2012;Abedietal.2014; Basto et al. 2015a). Deter- grassland seed bank richness and composition has mining how these environmental factors influence received little attention (Basto et al. 2015a), al- seed survival and germination is therefore critical though recent work in acidic and calcareous grass- to understanding the population and community dy- lands in the UK suggests that seed persistence, as namics of seed bank forming species, and has im- well as seed bank richness and density decrease with plications for how seed banks are managed in resto- increasing pH and is potentially mediated by patho- ration ecology (Khurana and Singh 2001). gens (Basto et al. 2015b). Little is known about how In this study, we have proposed a conceptual soil environmental factors potentially affect the spe- model of the direct and indirect effect of soil envi- cies composition, density and richness of soil seed ronmental factors on seed survival and species com- banks across the biomes. position of soil seed bank through have direct effects On the Tibetan plateau diverse grassland and on aboveground plant community and belowground meadow ecosystems have been recognized that re- soil microbes (Fig. 1). Soil seed bank is a balance of flect variation in elevation, moisture regime and soil seed input and seed output (Fenner 1985). Plant properties (Qi et al. 2015). However, no study cur- communities have a direct effect (seed input) on soil rently exists to identify common trends in above- seed bank through seed dispersal. Soil microbes also ground vegetation and seed bank composition across have a direct effect to soil seed bank through path- these different vegetation types. While the effects of ogens invasion. Soil seed bank experiences a com- environmental factors on the seed bank of species plicated ecological process in the soil. However, this present in above-ground vegetation (transient seed process is often neglected. What role does soil en- bank) may be primarily determined via effects on vironment play during soil seed bank formation and local seed production (Fig. 1), environmental effects function is an unresolved scientific issue. on species absent from aboveground vegetation Abiotic environmental conditions in the soil may (persistent seed bank) may instead primarily repre- not only have a direct impact on ungerminated seeds, sent direct effects on seeds mediated over long pe- but also influence seed survival via effects on seed riods by the soil physical and chemical environment, predators and pathogens (Fig. 1). In particular, fungal and by the action of seed predators and pathogens. pathogens have been shown to reduce seed survival The objectives of this paper were therefore: (1) in many habitats (e.g. Dalling et al. 1998; Schafer To determine how the density, richness and compo- and Kotanen 2003, 2004;O’Hanlon-Manners and sitional similarity of seed banks compares to above- Kotanen 2006), with the activity of soil fungi linked ground communities across six Tibetan plateau veg- to soil moisture and other soil characteristics (Blaney etation types, and (2) To determine how seed bank and Kotanen 2001; Schafer and Kotanen 2004; density, richness and species composition varies in Mordecai 2012). As a consequence, wetter habitats relation to soil moisture and soil chemical variables may have lower seed survival at least in part due to (pH, nitrogen and phosphorus availability, and soil the effect of fungal pathogens, while arid habitats organic carbon). Specifically, we hypothesized, that have longer-lived seed banks (Mordecai 2012;Kaiser the richness and density of the persistent seed bank and Pirhofer-Walzl 2015). would be negatively affected by increasing soil Previous work has also shown that soil chemical moisture and pH reflecting increasing losses of variables are often correlated with soil microbial seeds under conditions that favor seed infection communities across space (e.g. Nilsson et al. 2007; and decay by soil microbial communities. Plant Soil

Fig. 1 Conceptual model of direct and indirect effects of soil Plant community environmental factors on soil seed banks

Seed dispersal Direct effect Indirect effect Direct effect

Soil environmental factors Soil seed bank (Soil moisture, pH, SOM, TN, TP, AN, AP…)

Direct effect Indirect effect Direct effect Pathogen infection

Soil microbes

Materials and methods 100 m × 100 m plots at least 1000 m apart were selected at random. Each plot was considered an independent Study sites spatial replicate in each site. We randomly chose three subplots (10 m × 10 m) in each plot, and 10 cylindrical The study was carried out in the northeastern part of soil cores (3.6 cm diameter) were taken randomly from Tibetan Plateau in Gansu Province, P.R China (101°06′- each subplot for a total sample area of 0.092 m2 and the 103°33′E, 33°22′-35°24′N, about 37,000 km2,altitude total volume was 0.0092 m3 per site. The soil cores were 2339–4038 m a.s.l). We chose six vegetation types divided into two fractions: the upper layer (0–5cm (Table S1 in Appendix S1, Table 1), classified as AM depth), and lower layer (5–10 cm depth). Further, the (alpine meadow, 27 sites), ASM (alpine scrub meadow, 10 cores from each depth were then pooled to generate 8 sites), SM (swamp meadow, 7 sites), SAM (subalpine one representative sample per subplot. In total, there meadow, 6 sites), UG (upland grassland, 5 sites), and were 18 samples from each site, 57 sites and 1026 soil FEM (forest edge meadow, 4 sites). The different veg- samples. All the soil samples were transported to the etation types have been distinguished based on species Hezuo Research Station and placed under a north-facing composition, and the identity of dominant species (Wu window of direct exposure at ambient room tempera- 1980). To examine the characteristics of the soil seed tures for 15 days to allow them to dry. Then, samples bank community, a germination experiment was con- were kept dry in a dark storeroom of Hezuo Research ducted in the Research Station of Alpine Meadow and StationwithnoheatuntilMay2010. Wetland Ecosystems of Lanzhou University (Hezuo Branch), Gansu Province of China (N34°55′, Characterization of the germinable soil seed bank E102°53′, altitude 2900 m a.s.l), which was also located on the northeastern part of Tibetan Plateau. The mean We used the seedling emergence method to evaluate the annual temperature is 2.0 °C and the mean annual germinable species composition of the seed bank (e.g. precipitation is 560 mm. ter Heerdt et al. 1996). To reduce sample volume, each sample was sieved to remove plant fragments, coarse Soil seed bank sampling debris, and stones, then sieved through a coarse (4 mm mesh), and then a fine (0.2 mm mesh) sieve (Ma et al. To sample the persistent seed bank, soil samples were 2013b). Material retained on the 0.2 mm mesh was collected in August 2009, after the spring germination spread on to plastic germination trays (30 × 30 cm) filled flush, but before dispersal of the current season’sseeds. with a 15 cm deep layer of sand previously sterilized at Randomly selected replicate sites were established in 140 °C for 24 h. Twenty control trays containing steril- each vegetation type. Within each site, three ized sand were set up alongside the experimental trays to Plant Soil

Table 1 Short description of the vegetation types investigated

Vegetation type Extent Elevation Dominant species

Alpine scrub meadow 34°07′-35°.05′ 3205-3576 m Potentilla fruticosa, Spiraea alpina, ASM 101°06′-103°17′ Salix cupularis, Scirpus pumilus, Elymus dahuricus, Stipa capillata Alpine meadow 33°22′-35°04′ 3158-4002 m Elymus dahuricus, Kobresia capillifolia, AM 101°23′-103°18′ Polygonum viviparum, Poa poophagorum, Potentilla fragarioides, Anemone rivularis Subalpine meadow 34°46′-35°24′ 2039-3219 m Roegneria kamoji, Polygonum viviparum, SAM 102°20′-103°14′ Medicago ruthenica, Scirpus pumilus, Saussurea nigrescens, Kobresia humilis Swamp meadow 33°462–35°199 3488-3612 m Potentilla anserina, Agrostis hugoniana, SM 101°52′-103°10′ Carex zekogensis, Blysmus sinocompressus, Kobresia tibetica, Kobresia capillifolia. Forest edge meadow 34°22′-35°12′ 2631-3032 m Kobresia humilis, Carex zekogensis, FEM 102°47′-103°33′ Medicago ruthenica, Bupleurum smithii, Kobresia capillifolia Upland grassland 34°15′-35°21 2402-2969 m Brachypodium sylvaticum, Stipa capillacea, UG 102°48′-103°47′ Heteropappus hispidus, Bromus sinensis, Medicago ruthenica, Agropyron cristatum,

check for contamination from wind-dispersed seeds. diameter × 0–15 cm deep) were mixed to generate a Trays were watered and monitored several times a week. single soil sample in each subplot, resulting in nine Emerging seedlings were noted and removed once they composite soil samples from each site. Overall, 513 could be identified. The seedlings that could not be composite soil samples [3 subplots × 3 plots ×57 identified were grown separately until identification sites = 513 mixed soil samples] were used to analyze was possible. We turned over the soil regularly to facil- soil characteristics. For this study, soil moisture, soil pH, itate the germination of seeds from soil samples. After total nitrogen (N), soil available N (sum of nitrate and 5months(May10th– Oct 10th), the germination trial ammonium), total phosphorus (P), available P, and soil was ended as no more seedlings emerged after three organic matter (SOM) were measured. After removal of consecutive weeks. roots, each composite soil sample was sieved to <2 mm and split into two sub-samples. One sub-sample was + Plant community sampling kept at 4 °C for analyses of soil available N (NH4 -N — and NO3 N), and available phosphorus measurements Aboveground plant communities were surveyed in Au- within 1 month of collection. The other sub-sample was gust 2009, at the peak of plant development. Three air-dried at room temperature and ground to <0.15 mm 50 cm × 50 cm quadrats, corresponding to the location for soil pH, SOM, total N, and total P. of soil seed bank samples within each plot, were used to Soil moisture before air drying was obtained by the record the richness, composition and cover of the above- oven-drying method. Soil pH was measured using a pH ground plant community. In total, 171 quadrats were meter with a glass electrode (soil/KCl ratio 1:2.5). Soil examined. The Braun-Blanquet scale was used to esti- organic matter was measured by the K2Cr2O7 method mate cover (Ma et al. 2013a, b). using the modified Kjeldahl wet digestion procedure of Miller and Keeney (1982). Total nitrogen was deter- Analysis of soil properties mined by the Kjeldahl method (Institute of Soil Science, Academia Sinica 1978). Total P was determined after

Three additional soil samples were collected from each digestion by HClO4 – H2SO4 (Parkinson and Allen subplot where aboveground plant communities were 2008) using molybdenum-blue colorimetry. For soil + − sampled. In August 2009, three cores (3.6 cm in available N (NH4 -N and NO3 -N), fresh soil equivalent Plant Soil to10gdrymasswasextractedwith2MKCl The final dataset contained 489 species and consisted of (soil:solution ratio of 1:5) and determined using a Flow 114 sites (one per site; 57 seed bank and 57 above- Solution® IV Analyzer (OI Analytical Corporation, ground community sites). We pooled all the seed bank USA). Soil available phosphorus was extracted by the and the vegetation samples per site. All ordinations and Bray method (Bray and Kurtz 1945). Bray Curtis dissimilarity indices were based on relative abundance data. PERMANOVA (Permutational multi- Data analysis variate analysis of variance) using Bray-Curtis dissimi- larities was used to statistically assess differences in One-way analysis of variance (ANOVA) was used to species composition among seed banks, aboveground compare species richness, abundance, and proportion of plant communities, and between seed banks and above- life forms (annual, biennial and perennial) across vege- ground vegetation across the six vegetation types. Final- tation types in aboveground vegetation, and seed densi- ly, canonical correspondence analysis (CCA) was used ty, species richness, and proportion of life forms in the to examine the relationship between soil environmental seed bank. Post-hoc comparisons between vegetation factors and species composition of the aboveground types were made using the LSD test. Data for each soil vegetation and seed bank. A Monte Carlo permutation depth (0–5 cm, 5–10 cm) and the whole seed bank (999 permutations) was used to identify axes with sig- sample (0–10 cm) were analyzed separately. We exam- nificant eigenvalues and species-environment correla- ined the relationship between aboveground species rich- tions. CCA, NMDS and PERMANOVAwere conducted ness and abundance with soil pH by calculating Pearson using the package vegan (Oksanen et al. 2007)intheR correlation coefficients. 3.0.1 (www.r-project.org). To explore the relationship between each environmental factor (soil moisture, pH, total and available N, total and available P, and soil organic matter) and seed Results bank characteristics (species richness and seed density) we used linear regression. Independent variables except Variation in aboveground vegetation and seed banks soil pH were log-transformed prior to these analyses. In across sites this step, we confirmed which soil environmental factors significantly correlated with seed bank characteristics. The standing plant communities sampled at the 57 We further used multiple linear regression analysis sites consisted of 396 species, belonging to 47 fam- (using backward elimination) to explore the combined ilies, of which 11.3% were annuals, 2.4% were effects of soil environmental factors on seed bank rich- biennials, and 86.3% were perennials. Three species ness and density. Dependent (seed bank) variables were could only be identified to genus level (one Artemi- seed bank richness and density. Independent (soil envi- sia sp. and two Pedicularis sp), seven to family ronmental factors) variables included in multiple regres- level (Caryophyllaceae sp., Chenopodiaceae sp., sions were those that were significantly correlated with Compositae sp., Brassicaceae sp., Orchidaceae sp., seed bank characteristics in one or more univariate Polygonaceae sp., and Ranunculaceae sp), and five analyses. Data were examined for normality and homo- remained unknown. These 15 species are not includ- geneity of variance prior to analysis, and all independent ed in life form tallies. The proportion of annual, variables (except soil pH), seed density in soil seed bank biennial and perennial species did not differ across and species abundance in plant community data were the six vegetation types. log-transformed. Aikake’s information criterion (AIC) No seedlings emerged in the seed bank control was used for model selection. All ANOVA tests and trays. During the study, a total of 11,726 seedlings linear regressions were performed with a SPSS 16.0. of 150 species, belonging to 26 families, germinated The similarity of species composition among seed from the soil samples. Of these species, 19.4% were banks, aboveground vegetation, and between seed bank annuals, 2.3% biennials, and 78.3% perennials. Sev- and aboveground vegetation across the different vegeta- en species could only be identified to the genus level tion types was visualized using non-metric multidimen- (Arenaria sp., Cerastium sp., Draba sp., Pedicularis sional scaling (NMDS). We calculate similarity matrices sp., Poa., sp Potentilla sp., and Saussurea sp), three using the Bray Curtis coefficient (Kindt and Coe 2005). to family level (one Ranunculaceae and two Plant Soil

100 4 F=5.584, p<0.0001 F=11.892, p<0.001

80 a a a 3 b a a ac bc c bc 60 bc b 2 40

1 Species richness Species 20 Abundance (log scale)Abundance

0 0 ASMAMSAMSMFEMUG ASM AM SAM SM FEM UG Vegetation type Vegetation type Fig. 2 Species richness and abundance (per three quadrats, 0.75 m2) of aboveground plant communities. Letters indicate significant differences (ANOVA, Tukey range test) of species richness and abundance in the different vegetation types

Poaceae), and 11 were unidentified. These 21 (Fig. 2). The highest abundances were found in ASM, species are not included in life form tallies. The AM and SM, the lowest in UG. Species richness proportion of species in different life forms did not (r = −0.28, p = 0.034) and abundance (r = −0.49, differ across vegetation types, but the proportion of p < 0.001) of aboveground vegetation was significantly annuals and biennials in the seed bank (18.96 ± 0.67 negatively correlated with soil pH. %; mean ± 1 SE) was significantly higher than in Seed bank species richness differed significantly aboveground vegetation (9.45 ± 0.56%) (F =119.11, among the six vegetation types only in the 0–5cmsoil p <0.001). layer (Fig. 3). Seed density also only differed significant- Species richness per quadrat differed significantly ly among vegetation types in the 0–5 cm soil layer. The among the different vegetation types (Fig. 2), with the lowest seed density was in UG (747 ± 87 seeds m−2), and highest species richness in ASM, AM and SAM, and the there were no differences among other five vegetation lowest in SM, FEM and UG. Plant community abun- types (969 ± 150–1623 ± 184 seeds m−2;Fig.3). dance, i.e. the total number of individuals per quadrat Aboveground vegetation composition varied (0.25m2), differed significantly among vegetation types among six vegetation types (r2 = 0.240, p =0.001)

3.8 0-5cm. F=2.572, p=0.037 0-5cm. F=2.467, p=0.044 5-10cm. F=1.334, p=0.265 5-10cm. F=0.600, p=0.700 0-10cm. F=2.227, p=0.065 40 0-10cm. F=1.506, p=0.204 3.6 3.4

30 3.2 a a ab a bc 3.0 a bc ab b abc ab cd 20 2.8

Species richness 2.6 Seed density (Log scale) (Log density Seed 10 2.4 ASMAMSAMSMFEMUG ASMAMSAMSMFEMUG Vegetation type Vegetation type Fig. 3 Variation in species richness (per plot) and density (per richness (left) and seed density (right) among vegetation types in m−2) of soil seed banks among vegetation types. Seed density 0-5 cm soil layer. ASM alpine scrub meadow, AM alpine meadow, comparisons were carried out using log-transformed data. Letters SAM subalpine meadow, SM swamp meadow, FEM forest edge indicate significant differences (ANOVA, LSD test) in species meadow, UM upland grassland Plant Soil

(Fig. 4a). Pairwise comparisons of vegetation types aboveground vegetation. Most significant differ- were all significant except for AM and ASM, FEM ences were attributable to the ASM and AM types andSAM,FEMandUG,andSAMandUG (Table 2). (Table 2). There was strong separation of aboveground vegetation of the different vegetation types Similarity of species composition between the seed along the first two axes of the NMDS, with the bank and aboveground vegetation exception of AM and ASM communities, where there was no clear difference in community compo- In total there were 104 species in common between sition (Fig. 4b). Overall, species composition of seed the standing vegetation and soil seed bank. Overall, bank showed a significant difference among six similarity between the species composition of the vegetation types (r2 = 0.162, p = 0.001). However, seed bank and aboveground vegetation was low in based on the observed clustering in the NMDS or- each of six vegetation types (Fig. 4). Also, based on dination, and PERMANOVA pairwise comparison the result of PERMANOVA, similarity between seed (Fig. 4b, Table 2), the seed bank samples were much bank and aboveground vegetation showed a signifi- more similar across vegetation types than the cant difference in each of six vegetation types

Fig. 4 Two-dimensional nonmetric multidimensional scaling seed bank (sb) and aboveground vegetation (ve) combined (Stress (NMDS) ordination of species composition of the six vegetation value = 0.17) based on sampling 57 plant communities. The types based on abundance data for a aboveground vegetation Ordination was based on relative abundance data. Abbreviations (Stress value = 0.19), b seed banks (Stress value = 0.24), and c as in Table 1 Plant Soil

Table 2 Pairwise comparisons of vegetation types using (Veg), and in similarities between seed banks and aboveground PERMANOVA (Permutational multivariate analysis of variance) vegetation (Sim) are shown for the six vegetation types. Abbrevi- based on Bray-Curtis dissimilarity. Differences in species compo- ations of vegetation types as in Table 1 sition between seed banks (SB), aboveground plant communities

Veg ASM AM SAM SM FEM UG Type r2 pr2 pr2 pr2 pr2 Pr2 P

ASM SB ———————————— Veg ———————————— Sim 0.505 0.001 —————————— AM SB 1.739 0.042 —————————— Veg 1.443 0.074 —————————— Sim ——0.407 0.001 ———————— SAM SB 2.152 0.007 1.222 0.200 ———————— Veg 2.140 0.003 1.879 0.012 ———————— Sim ————0.367 0.003 —————— SM SB 2.385 0.002 2.255 0.008 1.685 0.079 —————— Veg 4.542 0.001 6.481 0.001 4.183 0.002 —————— Sim ——————0.429 0.001 ———— FEM SB 2.426 0.003 1.645 0.069 0.805 0.614 1.211 0.310 ———— Veg 3.288 0.002 3.429 0.001 1.123 0.259 3.077 0.006 ———— Sim ————————0.397 0.020 —— UG SB 3.862 0.001 3.088 0.001 1.192 0.238 2.296 0.014 0.837 0.661 —— Veg 3.953 0.001 4.221 0.001 1.268 0.167 4.023 0.001 1.147 0.281 —— Sim ——————————0.377 0.010

(Table 2). The NMDS showed that SAM-ve, AM-ve and available nitrogen (p = 0.008) were significantly correlat- ASM-ve grouped more closely to their seed bank groups ed with either of the first two ordination axes (Table 3). than did FEM-ve, UG-ve, and SM-ve (Fig. 4c). Table 3 CCA analysis of association of environmental factors Relationship between soil environmental factors (TN: total nitrogen, AN: available nitrogen, TP: total phosphorus, and community composition of aboveground vegetation AP: available phosphorus, SOM: soil organic matter) with and seed banks aboveground vegetation and seed bank composition Variable df Vegetation Seed bank Overall, a relatively small proportion of the variance in aboveground vegetation and seed bank community com- FpFp position(18.8%invegetationand15.4%inseedbank) Moisture 1 3.5949 0.0001 1.9632 0.005 was accounted for in the CCA, with 10% (vegetation) pH 1 1.4689 0.037 1.5044 0.035 and 7.6% (seed bank) explained by the first two axes. TN 1 1.5054 0.024 0.7936 0.883 Nonetheless, there was a significant relationship between community composition and environmental variables TP 1 1.1773 0.172 1.0959 0.424 (standing vegetation p =0.001;seedbankp =0.009). AN 1 1.1364 0.207 1.7089 0.008 Three variables for standing vegetation: soil moisture AP 1 1.2252 0.209 0.9499 0.454 (p =0.001),pH(p = 0.037) and total nitrogen SOM 1 1.2429 0.143 1.0027 0.552 (p = 0.024), and three variables for seed bank composi- Residual 49 tion: soil moisture (p = 0.005), pH (p = 0.035), total soil Differences shown in bold are statistically significant (P <0.05) Plant Soil

Relationship between soil environmental factors Discussion and species richness and seed density in the soil seed bank Composition of soil seed banks in the eastern Tibetan Plateau Seed density was significantly negatively correlated with soil pH and positively correlated with We found that the species composition of the soil seed soil moisture and soil organic matter (Fig. 5). bank differed significantly among the six vegetation Environmental variables soil pH, soil moisture types, reflecting differences in aboveground community and SOM were also correlated with each other. composition. Nonetheless, the seed bank samples were Soil pH was negatively correlated with soil mois- much more similar across vegetation types than were the ture (r = −0.648, p <0.001)andSOC aboveground samples, indicated by their strong cluster- (r = −0.464, p < 0.001), soil moisture was sig- ingintheNMDSordinationandPERMANOVA nificantly positively correlated with SOM pairwise comparison. Several mechanisms might con- (r =0.429,p <0.001). tribute to this. Firstly, the aboveground vegetation was Multiple regression analysis of seed bank re- dominated by perennials, which contribute little to the sponses to environmental variables showed that seed bank in late successional grassland ecosystems on for seed bank density a model including pH alone the Tibetan Plateau (Ma et al. 2009, 2013b). Secondly, had the lowest AIC value (Table 4).For seed bank in previous studies in alpine areas on the Tibetan Pla- richness, none of the environmental variables teau, we found that the soil seed banks were dominated were significant predictors (Table 4). by species characteristic of early successional commu-

4.0 r=-0.378, p=0.004 4.0 r=0.271, p=0.042

3.8 3.8

3.6 3.6

3.4 3.4

3.2 3.2

3.0 3.0 Seeddensity (log scale) Seed density(log scale)

2.8 2.8 56789 -1.2 -1.0 -.8 -.6 -.4 -.2 0.0 .2 .4 Soil pH Soil moisture (log scale)

4.0 r=0.350, p=0.021

3.8

3.6

3.4

3.2

3.0 Seeddensity (log scale)

2.8 .6 .8 1.0 1.2 1.4 1.6 Soil organic matter (log scale) Fig. 5 Significant relationships between soil environmental factors and seed density of soil seed bank Plant Soil

Table 4 Multiple regression of effects of soil organic matter (SOM), soil moisture (Moi), and soil pH (pH) on soil seed bank density and species richness. Aikake’s information criterion (AIC) was used for model selection. All variables except the pH were log-transformed

Independent variables Dependent variables

Density Rich

Model r p AIC r p AIC

SOM + Moi + pH 0.406 0.022 −109.3 0.234 0.389 −76.6 SOM + Moi −−− 0.234 0.218 −76.6 SOM + pH 0.406 0.008 −109.3 −−− SOM −−− 0.215 0.107 −76.0 pH 0.378 0.004 −109.1 −−− nities. These species produce long-lived seeds that have Relationship between soil environmental factors the potential to persist in the soil through succession and seed banks (Ma et al. 2009, 2011, 2013b). We found that three correlated variables: soil moisture, Similarity between soil seed bank and plant community soil pH, and total soil available N were significantly across vegetation types associated with the composition of soil seed banks, while soil moisture, soil pH, total N were associated This study is the first report that identifies the trends in with the composition of aboveground vegetation similarity between seed bank and aboveground vegeta- (Table 3). This suggests that both aboveground vegetation across different vegetation types on the Tibetan tion and seed bank communities are structured by soil Plateau, with significance for the restoration of different moisture and soil pH. Additional effect of total soil vegetation types in the region. We found that similarity inorganic N on seed bank composition suggests that in species composition between the seed bank and environmental effects on seed banks may not only in- aboveground vegetation differed significantly (Table 2) fluence seed inputs, but potentially also mediate seed and uniformly low in all six vegetation types (Fig. 4), survival. consistent with results from other alpine studies (e.g. In addition to effects on seed bank community com- Arroyo et al. 1999). The low similarity may in part position, soil pH was negatively correlated with seed reflect the relatively short growing season, resulting in bank density (Table 4,Fig.5). To date, contrasting a greater reliance on clonal regeneration than seedling effects of pH on seed bank density, richness and persis- recruitment (Körner 1999; Bueno et al. 2011). In addi- tence have been reported (Erenler et al. 2010,Pakeman tion, previous research on grasslands dominated by et al. 2012, Basto et al. 2015b). While Basto et al. perennial grasses has also found low similarities be- (2015b) found that seed density in acidic and calcareous tween the seed bank and the standing vegetation (Peco grasslands in England decreased with soil pH (range 3.5 et al. 1998;Arroyoetal.1999, Edwards and Crawley to 7). Using a seed burial experiment, Pakeman et al. 1999). In this study, we found perennial species domi- (2012) found seed survival was lower in soils with a nated the aboveground vegetation in the all six vegeta- lower pH (3.7–5.4), which they suggested may be due to tion types. These species rely almost exclusively on toxicity from aluminum or other metals. The following vegetative reproduction and contribute little to the seed mechanisms might account for the negative relation- bank (e.g. Ma et al. 2010a, b). Finally, the contribution ships of seed bank density with pH observed here. of seed banks to the aboveground plant community First, soil pH directly affects the growth and survival decreases through succession on the Tibetan plateau of species in the aboveground plant community due to (Ma et al. 2009, 2013b), and in other vegetation types effects on the availability of essential mineral nutrients, (Grandin 2001); the plant communities of each of six and on soil microbial communities (e.g. Stephenson and vegetation types in this study all represent mature plant Rechcigl 1991). Grime (1973) showed that the highest communities. species richness in aboveground vegetation in European Plant Soil grassland occurs at a soil pH range from 6.1 to 6.5, susceptible to high soil moisture are thought to suffer where most plant nutrients are in their most available from deleterious fungi and bacteria that are favored state, while species richness decreases with both in- under these conditions (Blaney and Kotanen 2001; creasing acidity and alkalinity. In this study, soil pH Schafer and Kotanen 2003). Here, we found a positive ranged from 5.6 to 8.5 across the 57 sites, with a mean relationship between moisture and seed density, sug- soil pH value of 6.8. Decreasing richness in the gesting that effects were mediate through responses of aboveground plant community was therefore aboveground vegetation. Consistent with this pattern, associated with increased alkalinity. Second, Basto the above-ground plant community richness also in- et al. (2015b) attributed the negative effect of increasing creased with soil moisture. Our results indicated that soil pH on seed density to a reduction in the abundance soil moisture has an indirect effect to seed survival via of grasses with increasing pH. However, using seed influences on the aboveground vegetation that produces burial experiments Basto et al. (2015b) also found that seed banks. Much research found fungal pathogens fungicide increased seed persistence but only when pH could reduce seed survival in many habitats (e.g. was >5.6. This suggests that seed persistence could Dalling et al. 1998, Schafer and Kotanen 2003, 2004, decrease at high pH in part due to increasing losses to O’Hanlon-Manners and Kotanen 2006), while soil fungal pathogens. In addition, decomposition processes moisture influence the activity of soil fungi (Schafer at high pH may also have a negative effect on seed and Kotanen 2004;Mordecai2012). Soil moisture has persistence (Bekker et al. 1998). In grasslands bacterial an indirect effect on seed bank density through soil fungi growth also increases at higher pH (Fernández-Calvinõ pathogens. We think the potential mechanism is the rates et al. 2011). Hence, moderately acidic soils could slow of decomposition tend to be low in cool alpine environ- the decomposition rate, and be more conducive to seed ments and therefore rates of seed loss from decay are persistence in the soil. expected to be low (McGraw and Vavrek 1989). In this While pH is one of the main drivers of species study, although soil biotic community hypothesis has composition and diversity of grassland ecosystems been addressed in our conceptual model (Fig. 1), we (Kalusová et al. 2009;Michalcováetal.2011), it fre- don’ t have any soil biotic data. Hence, it will be further quently correlates with other soil environmental factors explored in subsequent research. that may be responsible for relationships with seed bank characteristics(Valkó et al. 2014). Often, lower pH soils Acknowledgements We would like to thank Zhengkuan Gu, are associated with higher soil moisture content and Wei Li for their help with field assistance. The study was funded by the Natural Science Foundation of China (41671246), the increased soil organic matter accumulation related to Fundamental Research Funds for the Central Universities reduced rates of decomposition (Brady and Weil (lzujbky-2015-k15), and project of State Key Laboratory of Grass- 2008). However, in this study, the more alkaline soils land Agro-ecosystems of Lanzhou University (SKLGAE201706). had both higher moisture and organic matter contents, resulting in positive correlations with seed bank density References and richness (Fig. 5). We found that SOM correlated with seed bank density. 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